Merge pull request #1591 from FBruzzesi/patch/typos

Fix: Typos

Author: CamDavidsonPilon

README.md

@@ -24,8 +24,8 @@ If you are new to survival analysis, wondering why it is useful, or are interest
 
 ## Contact
  - Start a conversation in our [Discussions room](https://github.com/CamDavidsonPilon/lifelines/discussions).
- - Some users have posted common questions at [stats.stackexchange.com](https://stats.stackexchange.com/search?tab=votes&q=%22lifelines%22%20is%3aquestion)
- - creating an issue in the [Github repository](https://github.com/camdavidsonpilon/lifelines).
+ - Some users have posted common questions at [stats.stackexchange.com](https://stats.stackexchange.com/search?tab=votes&q=%22lifelines%22%20is%3aquestion).
+ - Creating an issue in the [Github repository](https://github.com/camdavidsonpilon/lifelines).
 
 ## Development
 

docs/Changelog.rst

@@ -1063,7 +1063,7 @@ New features
 -  new ``lifelines.plotting.rmst_plot`` for pretty figures of survival
    curves and RMSTs.
 -  new variance calculations for
-   ``lifelines.utils.resticted_mean_survival_time``
+   ``lifelines.utils.restricted_mean_survival_time``
 -  performance improvements on regression models’ preprocessing. Should
    make datasets with high number of columns more performant.
 
@@ -1760,7 +1760,7 @@ Bug fixes
 -------------------
 
 -  Some performance improvements to ``CoxPHFitter`` (about 30%). I know
-   it may seem silly, but we are now about the same or slighty faster
+   it may seem silly, but we are now about the same or slightly faster
    than the Cox model in R’s ``survival`` package (for some testing
    datasets and some configurations). This is a big deal, because 1)
    lifelines does more error checking prior, 2) R’s cox model is written
@@ -1800,7 +1800,7 @@ New features
 API changes
 ~~~~~~~~~~~
 
--  ``inital_beta`` in Cox model’s ``.fit`` is now ``initial_point``.
+-  ``initial_beta`` in Cox model’s ``.fit`` is now ``initial_point``.
 -  ``initial_point`` is now available in AFT models and
    ``CoxTimeVaryingFitter``
 -  the DataFrame ``confidence_intervals_`` for univariate models is
@@ -1853,7 +1853,7 @@ New features
 
 -  new AFT models: ``LogNormalAFTFitter`` and ``LogLogisticAFTFitter``.
 -  AFT models now accept a ``weights_col`` argument to ``fit``.
--  Robust errors (sandwich errors) are now avilable in AFT models using
+-  Robust errors (sandwich errors) are now available in AFT models using
    the ``robust=True`` kwarg in ``fit``.
 -  Performance increase to ``print_summary`` in the ``CoxPHFitter`` and
    ``CoxTimeVaryingFitter`` model.
@@ -2066,7 +2066,7 @@ Bug Fixes
    Series now (use to be numpy arrays)
 -  remove ``alpha`` keyword from all statistical functions. This was
    never being used.
--  Gone are astericks and dots in ``print_summary`` functions that
+-  Gone are asterisks and dots in ``print_summary`` functions that
    represent signficance thresholds.
 -  In models’ ``summary`` (including ``print_summary``), the ``log(p)``
    term has changed to ``-log2(p)``. This is known as the s-value. See
@@ -2105,7 +2105,7 @@ Bug Fixes
 -------------------
 
 -  Fix in ``compute_residuals`` when using ``schoenfeld`` and the
-   minumum duration has only censored subjects.
+   minimum duration has only censored subjects.
 
 .. _section-91:
 
@@ -2154,7 +2154,7 @@ Bug Fixes
    -  ``weights_col`` is added
    -  ``nn_cumulative_hazard`` is removed (may add back)
 
--  some plotting improvemnts to ``plotting.plot_lifetimes``
+-  some plotting improvements to ``plotting.plot_lifetimes``
 
 .. _section-94:
 
@@ -2203,7 +2203,7 @@ Bug Fixes
 -  ``statistics.pairwise_logrank_test`` now returns a
    ``StatisticalResult`` object instead of a nasty NxN DataFrame 💗
 -  Display log(p-values) as well as p-values in ``print_summary``. Also,
-   p-values below thesholds will be truncated. The orignal p-values are
+   p-values below thresholds will be truncated. The original p-values are
    still recoverable using ``.summary``.
 -  Floats ``print_summary`` is now displayed to 2 decimal points. This
    can be changed using the ``decimal`` kwarg.
@@ -2275,7 +2275,7 @@ Bug Fixes
    https://www.cs.cmu.edu/~pradeepr/convexopt/Lecture_Slides/Newton_methods.pdf.
    Details about the Newton-decrement are added to the ``show_progress``
    statements.
--  Minimum suppport for scipy is 1.0
+-  Minimum support for scipy is 1.0
 -  Convergence errors in models that use Newton-Rhapson methods now
    throw a ``ConvergenceError``, instead of a ``ValueError`` (the former
    is a subclass of the latter, however).
@@ -2317,7 +2317,7 @@ Bug Fixes
    that people could compute the p-values by hand incorrectly, a worse
    outcome I think. So, this is my stance. P-values between 0.1 and 0.05
    offer *very* little information, so they are removed. There is a
-   growing movement in statistics to shift “signficant” findings to
+   growing movement in statistics to shift “significant” findings to
    p-values less than 0.01 anyways.
 -  New fitter for cumulative incidence of multiple risks
    ``AalenJohansenFitter``. Thanks @pzivich! See “Methodologic Issues
@@ -2458,7 +2458,7 @@ Bug Fixes
 -  added ``step_size`` param to ``CoxPHFitter.fit`` - the default is
    good, but for extremely large or small datasets this may want to be
    set manually.
--  added a warning to ``CoxPHFitter`` to check for complete seperation:
+-  added a warning to ``CoxPHFitter`` to check for complete separation:
    https://stats.idre.ucla.edu/other/mult-pkg/faq/general/faqwhat-is-complete-or-quasi-complete-separation-in-logisticprobit-regression-and-how-do-we-deal-with-them/
 -  Additional functionality to ``utils.survival_table_from_events`` to
    bin the index to make the resulting table more readable.
@@ -2480,7 +2480,7 @@ Bug Fixes
 0.11.2
 ------
 
--  Changing liscense to valilla MIT.
+-  Changing license to valilla MIT.
 -  Speed up ``NelsonAalenFitter.fit`` considerably.
 
 .. _section-114:
@@ -2555,7 +2555,7 @@ Bug Fixes
 -----
 
 -  deprecates Pandas versions before 0.18.
--  throw an error if no admissable pairs in the c-index calculation.
+-  throw an error if no admissible pairs in the c-index calculation.
    Previously a NaN was returned.
 
 .. _section-120:
@@ -2749,7 +2749,7 @@ Bug Fixes
    Also some good speed improvements.
 -  KaplanMeierFitter and NelsonAalenFitter now have a ``_label``
    property that is passed in during the fit.
--  KaplanMeierFitter/NelsonAalenFitter’s inital ``alpha`` value is
+-  KaplanMeierFitter/NelsonAalenFitter’s initial ``alpha`` value is
    overwritten if a new ``alpha`` value is passed in during the ``fit``.
 -  New method for KaplanMeierFitter: ``conditional_time_to``. This
    returns a DataFrame of the estimate: med(S(t \| T>s)) - s, human

docs/Examples.rst

@@ -941,7 +941,7 @@ In both cases, the reported standard errors from a unadjusted Cox model will be
     rossi = load_rossi()
 
     # this may come from a database, or other libraries that specialize in matching
-    mathed_pairs = [
+    matched_pairs = [
         (156, 230),
         (275, 228),
         (61, 252),

docs/Survival Regression.rst

@@ -111,7 +111,7 @@ New in v0.25.0, We can also use ✨formulas✨ to handle the right-hand-side of
 
     cph.fit(rossi, duration_col='week', event_col='arrest', formula="fin + wexp + age * prio")
 
-is analgous to the linear model with interaction term:
+is analogous to the linear model with interaction term:
 
 .. math::
    \beta_1\text{fin} + \beta_2\text{wexp} + \beta_3 \text{age} + \beta_4 \text{prio} + \beta_5 \text{age} \cdot \text{prio}
@@ -513,7 +513,7 @@ Below we compare the non-parametric and the fully parametric baseline survivals:
 
     ax = cph_spline.baseline_cumulative_hazard_[bch_key].plot(label="spline")
     cph_semi.baseline_cumulative_hazard_[bch_key].plot(ax=ax, drawstyle="steps-post", label="semi")
-    cph_piecewise.baseline_cumulative_hazard_[bch_key].plot(ax=ax, label="peicewise[20,35]")
+    cph_piecewise.baseline_cumulative_hazard_[bch_key].plot(ax=ax, label="piecewise[20,35]")
     plt.legend()
 
 

docs/Time varying survival regression.rst

@@ -78,7 +78,7 @@ The new dataset looks like:
     +--+-----+----+----+-----+
 
 
-You'll also have secondary dataset that references future measurements. This could come in two "types". The first is when you have a variable that changes over time (ex: administering varying medication over time, or taking a tempature over time). The second types is an event-based dataset: an event happens at some time in the future (ex: an organ transplant occurs, or an intervention). We will address this second type later. The first type of dataset may look something like:
+You'll also have secondary dataset that references future measurements. This could come in two "types". The first is when you have a variable that changes over time (ex: administering varying medication over time, or taking a temperature over time). The second types is an event-based dataset: an event happens at some time in the future (ex: an organ transplant occurs, or an intervention). We will address this second type later. The first type of dataset may look something like:
 
 Example: